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Integrating genomics and modelling to predict climate change response and identify drivers of decline in the endangered freshwater pearl mussel (Margaritifera margaritifera)

School of Biological Sciences

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Dr Kara Layton , Prof S Piertney , Dr L Lancaster , Dr Victoria Pritchard No more applications being accepted Funded PhD Project (UK Students Only)
Aberdeen United Kingdom Bioinformatics Climate Science Ecology Environmental Biology Evolution Genetics Molecular Biology Zoology

About the Project

Accurate predictions of climate change response are needed to protect global biodiversity that continues to be impacted by environmental change. Recent work has demonstrated the utility of genomics for improving these predictions (Waldvogel et al., 2019), and while these methods are applicable to all ecosystems and taxa, they are particularly critical for species that are of national importance. Mussels are important to the health of freshwater ecosystems because of their role in water purification and nutrient cycling (Vaughn, 2017), but they are also among the most endangered animals on the planet. The freshwater pearl mussel, Margaritifera margaritiera, was once widely distributed across the Northern Hemisphere but is now considered rare across most of its range. Some of the largest remaining populations exist in northwest Scotland (Cosgrove et al. 2016), making it a pressing conservation priority for the region. Several factors have contributed to their decline, including pollution, pearl fishing and low density of salmonids that serve as hosts for the larval stage, but the role of climate in this decline is largely unknown and critical to understand.

Despite the importance of genetic adaptation in dictating climate change response, predictions often rely on ecological niche models that use occurrence and environmental data to predict the future geographical range of a species (Feng et al., 2019), rather than considering adaptive potential and evolutionary resilience. Here, you will integrate genomics and modelling to predict how the pearl mussel will respond to climate change. You will use high-throughput DNA sequencing to assay genetic variation across populations in Scotland. You will extract environmental data from climate databases and use remote sensing equipment to collect river temperature data in Scotland. You will use genotype-environment association methods to identify a set of climate-associated loci that can be used in future management applications. Next, you will use machine learning to estimate ‘genomic vulnerability’, defined as the degree of mismatch between current and future genetic variation, to identify vulnerable populations. You will also use machine learning to determine which environmental, biological and anthropogenic variables are most important in explaining mussel decline. For this, you will use new and existing environmental data along with decline data that derives from a recent survey in Scotland (Watt et al. 2015). Lastly, you will perform ecological niche modelling to predict the future distribution of suitable habitat for the mussel under different climate change scenarios. With this, you can compare the range of future suitable habitat to the regions containing highly vulnerable populations and assess concordance among genomic and traditional methods of vulnerability assessment.

This project provides an outstanding opportunity for the student to, for the first time, employ integrative methods to predict climate change response in the endangered pearl mussel. Previous studies have demonstrated the utility of a combined genomics and modelling approach to identify highly vulnerable populations of vertebrate (e.g. Layton et al., in press), but this project will be the first to apply this to an endangered invertebrate model in an applied context. By identifying highly vulnerable populations across Scotland, isolating the drivers of mussel decline and predicting future suitable habitat, the student will contribute to the conservation of an iconic component of Scottish biodiversity.

You will benefit from a supervisory team with strengths in evolutionary biology, conservation genetics and aquatic biology and from a department with diverse research interests and a strong cohort of PhD students. You will gain valuable skills in fieldwork and data collection, molecular work, population genomics, ecological modelling, bioinformatics, science communication and writing, among other skills. You will be based at the University of Aberdeen but will have the opportunity to visit the UHI Rivers & Lochs Institute in Inverness and work alongside NatureScot. The University of Aberdeen offers a variety of social clubs for students and is committed to equality and diversity, while the region itself boasts extensive cultural and outdoor opportunities. We are looking for an enthusiastic student with a desire to learn new skills and an interest in integrating ecology and evolutionary theory for species conservation. You will hold a minimum 2:1 Honours degree (or equivalent) in biology, zoology, genetics or another relevant field. A master’s degree in a relevant subject with some experience in population genetics or modelling is desirable.

Funding Notes

The SUPER DTP has filled its international quota for recruitment for this round, therefore this project is only available to home (UK) students. Funding will cover UK tuition fees/stipend/research & training support grant only.
To apply:
-Apply for 'PhD in Biological Science'
-Enter the name of the lead supervisor when prompted
-Please ensure you submit ALL necessary higher education documents


Cosgrove et al., 2016. The status of the freshwater pearl mussel Margaritifera margaritifera in Scotland: extent of change since 1990s, threats and management implications. Biodivers Conserv, 25, 2093–2112.
Feng et al., 2019. A checklist for maximizing reproducibility of ecological niche models. Nat Ecol Evol, 3, 1382–1395.
Layton et al. In press. Genomic evidence for past and future climate-linked loss in a migratory Arctic fish. Nat Clim Change.
Vaughn, 2017. Ecosystem services provided by freshwater mussels. Hydrobiologia, 810,15–27.
Waldvogel et al. 2020. Evolutionary genomics can improve prediction of species’ responses to climate change. Evol Lett, 4, 4–18.
Watt et al. 2015. A national freshwater pearl mussel (Margaritifera margaritifera, L.) survey of Scotland. Scot Nat Heritage Report No. 901.

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